def grand_tour(gps, trgs, trgtype, windows, conf=0.68, plot=False):
"""
a function that compute the grand tour statistics
"""
if trgtype == "kw":
col = event.col_kw
snrkey = 'signif'
elif trgtype == "Omicron":
col = event.col_snglBurst
snrkey = 'snr'
else:
raise ValueError("do not understand trgtype=%s" % (trgtype))
figax = []
for win in windows:
print "window=%.6f" % win
### downelect to only this window
trgs = [
trg for trg in trgs if (trg[col['tcent']] >= gps - win) and (
trg[col['tcent']] <= gps + win)
]
Pvalue = np.infty
Snr = None
Dt = None
Frq = None
for snrThr in sorted(list(set([trg[col[snrkey]] for trg in trgs
]))): ### iterate over all snrs present
print "snrThr=%.6f" % snrThr
ctrgs = [trg for trg in trgs if trg[snrkey] >= snrThr]
n = len(ctrgs) ### number of triggers
r = 0.5 * n / opts.window ### point estimate of the rate
if n:
dt = np.array([ctrgs[col['tcent']] for trg in ctrgs]) - gps
arg = np.argmin(np.abs(dt))
snr = ctrgs[arg][col[snrkey]]
frq = ctrgs[arg][col['fcent']]
min_dt = dt[arg]
absmin_dt = abs(min_dt)
else:
min_dt = win
snr = None
frq = None
if r > 0:
pvalue = 1 - np.exp(
-r * 2 * absmin_dt
) ### cumulative probability of observing min_dt <= observed min_dt | estimated rate, the factor of 2 comes from looking on either side of the specified gps time
else:
pvalue = 1 ### limit is not great here...need to add CI
print "\n\tchannel=%s\n\t-> Ntrg=%d\n\t-> rate=%.9e Hz\n\t-> min_dt=%.9e sec\n\t-> pvalue=%.9e" % (
chan, n, r, min_dt, pvalue)
r_l, r_h = np.array(gci.poisson_bs(conf, n)) * 0.5 / win
pvalue_l = 1 - np.exp(-r_l * 2 * absmin_dt)
pvalue_h = 1 - np.exp(-r_h * 2 * absmin_dt)
print "\t-> %.5e confidence:\n\t\tlow rate =%.9e Hz\n\t\thigh rate=%.9e Hz\n\t\tlow pvalue =%.9e\n\t\thigh pvalue=%.9e" % (
conf, r_l, r_h, pvalue_l, pvalue_h)
if pvalue_h < Pvalue:
pvalue = pvalue_h
Snr = snr
Dt = dt
Frq = frq
### separate plot for each window
### xaxis : time (relative to gps)
### yaxis SNR
### color = pvalue (upper limit?)
if plot:
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
figax.append((fig, ax))
### plot all triggers
# dts = np.array([ trg[col['tcent'] for trg in trgs ]) - gps
# snrs = [ trg[col[snrkey]] for trg in trgs ]
# frqs = [ trg[col['fcent']] for trg in trgs ]
for trg in trgs:
dt = trg[col['tcent']] - gps
snr = trg[col[snrkey]]
frq = trg[col['fcent']]
color = snr_map(snr)
ax.plot(dt,
frq,
markerfacecolor=color,
markeredgecolor='none',
marker='o',
linestyle='none',
alpha=0.50,
markersize=2)
ax.set_xlabel('time relative to %.6f [sec]' % gps)
ax.set_ylabel('frequency [Hz]')
if Dt:
color = snr_map(Snr)
ax.plot(Dt,
Frq,
markerfacecolor='none',
markeredgecolor=color,
marker='o',
linestyle='none',
alpha=1.00,
markersize=5)
ax.text(Dt, Frq, '%.3e' % Pvalue, ha='left', va='center')
ax.set_xlim(xmin=-win, xmax=win)
ymax = 1
maxfrq = max([trg[col['fcent']] for trg in trgs])
while maxfrq > ymax:
ymax *= 2
ax.set_ylim(ymin=0, ymax=ymax)
return figax
min_dt = dt[arg]
else:
min_dt = opts.window
arg = None
absmin_dt = abs(min_dt)
if r > 0:
pvalue = 1 - np.exp(
-r * 2 * absmin_dt
) ### cumulative probability of observing min_dt <= observed min_dt | estimated rate, the factor of 2 comes from looking on either side of the specified gps time
else:
pvalue = 1 ### limit is not great here...need to add CI
if opts.confidence_intervals:
if n < opts.nmax:
r_l, r_h = np.array(gci.poisson_bs(conf, n)) * 0.5 / (opts.window - opts.exclude)
else:
s = n ** 0.5 * (
scipy.special.erfinv(conf) * 2 ** 0.5
) ### the size of the standard deviation times the number of standard deviations needed to cover conf
r_l = max(0, (n - s) * 0.5 / (opts.window - opts.exclude))
r_h = (n + s) * 0.5 / (opts.window - opts.exclude)
pvalue_l = 1 - np.exp(-r_l * 2 * absmin_dt)
pvalue_h = 1 - np.exp(-r_h * 2 * absmin_dt)
if pvalue <= opts.pvalue_print_thr:
print "\n\tchannel=%s\n\t-> Ntrg=%d\n\t-> rate=%.9e Hz\n\t-> min_dt=%.9e sec" % (chan, n, r, min_dt)
if arg != None:
trg = trgdict[chan][arg]
min_dt = dt[arg]
else:
min_dt = opts.window
arg = None
absmin_dt = abs(min_dt)
if r > 0:
pvalue = 1 - np.exp(
-r * 2 * absmin_dt
) ### cumulative probability of observing min_dt <= observed min_dt | estimated rate, the factor of 2 comes from looking on either side of the specified gps time
else:
pvalue = 1 ### limit is not great here...need to add CI
if opts.confidence_intervals:
if n < opts.nmax:
r_l, r_h = np.array(gci.poisson_bs(
conf, n)) * 0.5 / (opts.window - opts.exclude)
else:
s = n**0.5 * (
scipy.special.erfinv(conf) * 2**0.5
) ### the size of the standard deviation times the number of standard deviations needed to cover conf
r_l = max(0, (n - s) * 0.5 / (opts.window - opts.exclude))
r_h = (n + s) * 0.5 / (opts.window - opts.exclude)
pvalue_l = 1 - np.exp(-r_l * 2 * absmin_dt)
pvalue_h = 1 - np.exp(-r_h * 2 * absmin_dt)
if (pvalue <= opts.pvalue_print_thr):
print "\n\tchannel=%s\n\t-> Ntrg=%d\n\t-> rate=%.9e Hz\n\t-> min_dt=%.9e sec" % (
chan, n, r, min_dt)
if arg != None:
def grand_tour(gps, trgs, trgtype, windows, conf=0.68, plot=False):
"""
a function that compute the grand tour statistics
"""
if trgtype == "kw":
col = event.col_kw
snrkey = 'signif'
elif trgtype == "Omicron":
col = event.col_snglBurst
snrkey = 'snr'
else:
raise ValueError("do not understand trgtype=%s"%(trgtype))
figax = []
for win in windows:
print "window=%.6f"%win
### downelect to only this window
trgs = [ trg for trg in trgs if (trg[col['tcent']] >= gps-win) and (trg[col['tcent']] <= gps+win) ]
Pvalue = np.infty
Snr = None
Dt = None
Frq = None
for snrThr in sorted(list(set([ trg[col[snrkey]] for trg in trgs ]))): ### iterate over all snrs present
print "snrThr=%.6f"%snrThr
ctrgs = [ trg for trg in trgs if trg[snrkey] >= snrThr ]
n = len( ctrgs ) ### number of triggers
r = 0.5*n/opts.window ### point estimate of the rate
if n:
dt = np.array([ctrgs[col['tcent']] for trg in ctrgs]) - gps
arg = np.argmin(np.abs(dt))
snr = ctrgs[arg][col[snrkey]]
frq = ctrgs[arg][col['fcent']]
min_dt = dt[arg]
absmin_dt = abs(min_dt)
else:
min_dt = win
snr = None
frq = None
if r > 0:
pvalue = 1 - np.exp(-r*2*absmin_dt) ### cumulative probability of observing min_dt <= observed min_dt | estimated rate, the factor of 2 comes from looking on either side of the specified gps time
else:
pvalue = 1 ### limit is not great here...need to add CI
print "\n\tchannel=%s\n\t-> Ntrg=%d\n\t-> rate=%.9e Hz\n\t-> min_dt=%.9e sec\n\t-> pvalue=%.9e"%(chan, n, r, min_dt, pvalue)
r_l, r_h = np.array( gci.poisson_bs(conf, n) ) * 0.5 / win
pvalue_l = 1 - np.exp(-r_l*2*absmin_dt)
pvalue_h = 1 - np.exp(-r_h*2*absmin_dt)
print "\t-> %.5e confidence:\n\t\tlow rate =%.9e Hz\n\t\thigh rate=%.9e Hz\n\t\tlow pvalue =%.9e\n\t\thigh pvalue=%.9e"%(conf, r_l, r_h, pvalue_l, pvalue_h)
if pvalue_h < Pvalue:
pvalue = pvalue_h
Snr = snr
Dt = dt
Frq = frq
### separate plot for each window
### xaxis : time (relative to gps)
### yaxis SNR
### color = pvalue (upper limit?)
if plot:
fig = plt.figure()
ax = plt.subplot(1,1,1)
figax.append( (fig, ax) )
### plot all triggers
# dts = np.array([ trg[col['tcent'] for trg in trgs ]) - gps
# snrs = [ trg[col[snrkey]] for trg in trgs ]
# frqs = [ trg[col['fcent']] for trg in trgs ]
for trg in trgs:
dt = trg[col['tcent']] - gps
snr = trg[col[snrkey]]
frq = trg[col['fcent']]
color = snr_map( snr )
ax.plot( dt, frq, markerfacecolor=color, markeredgecolor='none', marker='o', linestyle='none', alpha=0.50, markersize=2 )
ax.set_xlabel( 'time relative to %.6f [sec]'%gps )
ax.set_ylabel( 'frequency [Hz]' )
if Dt:
color = snr_map( Snr )
ax.plot( Dt, Frq, markerfacecolor='none', markeredgecolor=color, marker='o', linestyle='none', alpha=1.00, markersize=5 )
ax.text( Dt, Frq, '%.3e'%Pvalue, ha='left', va='center' )
ax.set_xlim(xmin=-win, xmax=win)
ymax = 1
maxfrq = max( [ trg[col['fcent']] for trg in trgs ] )
while maxfrq > ymax:
ymax *= 2
ax.set_ylim(ymin=0, ymax=ymax)
return figax
